class_docstr: Apply a random transformation to the saturation of a tensor image.
  
  This implementation aligns PIL. Hence, the output is close to TorchVision.
  
  .. image:: _static/img/RandomSaturation.png
  
  Args:
      p: probability of applying the transformation.
      saturation: the saturation factor to apply.
      same_on_batch: apply the same transformation across the batch.
      keepdim: whether to keep the output shape the same as input (True) or broadcast it
               to the batch form (False).
  Shape:
      - Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`, Optional: :math:`(B, 3, 3)`
      - Output: :math:`(B, C, H, W)`
  
  .. note::
      This function internally uses :func:`kornia.enhance.adjust_saturation`
  
  Examples:
      >>> rng = torch.manual_seed(0)
      >>> inputs = torch.rand(1, 3, 3, 3)
      >>> aug = RandomSaturation(saturation = (0.5,2.),p=1.)
      >>> aug(inputs)
      tensor([[[[0.5569, 0.7682, 0.3529],
                [0.4811, 0.3474, 0.7411],
                [0.5028, 0.8964, 0.6772]],
      <BLANKLINE>
               [[0.6323, 0.5358, 0.5265],
                [0.4203, 0.2706, 0.5525],
                [0.5185, 0.7863, 0.8681]],
      <BLANKLINE>
               [[0.3711, 0.4989, 0.6816],
                [0.9152, 0.3971, 0.8742],
                [0.4636, 0.7060, 0.9527]]]])
  
  To apply the exact augmenation again, you may take the advantage of the previous parameter state:
  
      >>> input = torch.rand(1, 3, 32, 32)
      >>> aug = RandomSaturation((0.8,1.2), p=1.)
      >>> (aug(input) == aug(input, params=aug._params)).all()
      tensor(True)
code: def __init__(
      self,
      saturation: Tuple[float, float] = (1.0, 1.0),
      same_on_batch: bool = False,
      p: float = 1.0,
      keepdim: bool = False,
  ) -> None:
      super().__init__(
          p=p, same_on_batch=same_on_batch, keepdim=keepdim
      )
      self.saturation: Tensor = _range_bound(
          saturation, "saturation", center=1.0
      )
      self._param_generator = rg.PlainUniformGenerator(
          (self.saturation, "saturation_factor", None, None)
      )
  
file_path: kornia/kornia/augmentation/_2d/intensity/saturation.py
imports: from typing import Any, Dict, Optional, Tuple
  from kornia.augmentation import random_generator
  from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
  from kornia.augmentation.utils import _range_bound
  from kornia.core import Tensor
  from kornia.enhance.adjust import adjust_saturation
label_desc: Use image node_id 1 to create a new RandomSaturation object from inherited base classes: IntensityAugmentationBase2D with example: obj = RandomSaturation(saturation, same_on_batch, p, keepdim)